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        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

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        About MultiQC

        This report was generated using MultiQC, version 1.27

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/rrap analysis pipeline.
        Report generated on 2025-06-12, 15:26 BST based on data in: /hps/nobackup/rdf/metagenomics/service-team/users/timrozday/rrap_runs/mini1/work/a0/0dcea4da99cbbf47307d54f5253637

        General Statistics

        Showing 6/6 rows and 11/15 columns.
        Sample NameError rateNon-primaryReads mapped% Mapped% Proper pairs% MapQ 0 readsTotal seqsMean insert% Duplication% > Q30Mb Q30 basesReads After FilteringGC content% PF% Adapter
        ERR10889056
        0.0%
        96.4%
        2.4Mb
        0.0M
        48.2%
        99.4%
        2.0%
        ERR10889056_short_read_host
        4.26%
        0.0M
        0.0M
        0.0%
        0.0%
        0.0%
        0.0M
        105.0bp
        ERR10889056_short_read_phix
        0.00%
        0.0M
        0.0M
        0.0%
        0.0%
        0.0%
        0.0M
        0.0bp
        ERR10889147
        0.0%
        95.9%
        2.4Mb
        0.0M
        49.5%
        100.0%
        2.1%
        ERR10889147_short_read_host
        5.15%
        0.0M
        0.0M
        16.2%
        13.0%
        5.2%
        0.0M
        533.7bp
        ERR10889147_short_read_phix
        0.00%
        0.0M
        0.0M
        0.0%
        0.0%
        0.0%
        0.0M
        0.0bp

        Samtools

        Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        Created with MultiQC

        fastp

        All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

        Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

        Filtered Reads

        Filtering statistics of sampled reads.

        Created with MultiQC

        Insert Sizes

        Insert size estimation of sampled reads.

        Created with MultiQC

        Sequence Quality

        Average sequencing quality over each base of all reads.

        Created with MultiQC

        GC Content

        Average GC content over each base of all reads.

        Created with MultiQC

        N content

        Average N content over each base of all reads.

        Created with MultiQC